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检索条件"任意字段=2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 21008 条记录，以下是1511-1520 订阅

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SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters

SkinningNet: Two-Stream Graph Convolutional Neural Network f...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Mosella-Montoro, Albert Ruiz-Hidalgo, Javier Univ Politecn Cataluna Barcelona Spain

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This work presents SkinningNet, an end-to-end Two Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to generalize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives.

关键词： Shape Network topology Machine vision computer architecture Feature extraction Skeleton Graph neural networks

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Open Challenges in Deep Stereo: the Booster Dataset

Open Challenges in Deep Stereo: the Booster Dataset

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ramirez, Pierluigi Zama Tosi, Fabio Poggi, Matteo Salti, Samuele Mattoccia, Stefano Di Stefano, Luigi Univ Bologna Dept Comp Sci & Engn DISI CVLAB Bologna Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.

关键词： computer vision Three-dimensional displays Pipelines Sensors pattern recognition Labeling

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Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos

Weakly-Supervised Online Action Segmentation in Multi-View I...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ghoddoosian, Reza Dwivedi, Isht Agarwal, Nakul Choi, Chiho Dariush, Behzad Honda Res Inst San Jose CA 95134 USA Univ Texas Arlington VLM Lab Arlington TX 76019 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper addresses a new problem of weaklysupervised online action segmentation in instructional videos. We present a framework to segment streaming videos online at test time using Dynamic Programming and show its advantages over greedy sliding window approach. We improve our framework by introducing the Online-Offline Discrepancy Loss (OODL) to encourage the segmentation results to have a higher temporal consistency. Furthermore, only during training, we exploit framewise correspondence between multiple views as supervision for training weakly-labeled instructional videos. In particular, we investigate three different multi-view inference techniques to generate more accurate frame-wise pseudo ground-truth with no additional annotation cost. We present results and ablation studies on two benchmark multi-view datasets, Breakfast and IKEA ASM. Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.

关键词： Training computer vision Costs Annotations Computational modeling Benchmark testing pattern recognition

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Towards End-to-End Unified Scene Text Detection and Layout Analysis

Towards End-to-End Unified Scene Text Detection and Layout A...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Long, Shangbang Qin, Siyang Panteleev, Dmitry Bissacco, Alessandro Fujii, Yasuhisa Raptis, Michalis Google Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way. Comprehensive experiments show that our unified model achieves better performance than multiple well-designed baseline methods. Additionally, this model achieves stateof-the-art results on multiple scene tell detection datasets without the need of complex post-processing. Dataset and code: https://***/google-research-datasets/hiertext.

关键词： computer vision Analytical models Text analysis Codes Annotations Computational modeling Layout

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A Style-aware Discriminator for Controllable Image Translation

A Style-aware Discriminator for Controllable Image Translati...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Kunhee Park, Sanghun Jeon, Eunyeong Kim, Taehun Kim, Daijin Pohang Univ Sci & Technol POSTECH Pohang South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Current image-to-image translations do not control the output domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results. This limitation largely arises because labels do not consider the semantic distance. To mitigate such problems, we propose a style-aware discriminator that acts as a critic as well as a style encoder to provide conditions. The style-aware discriminator learns a controllable style space using prototype-based self-supervised learning and simultaneously guides the generator. Experiments on multiple datasets verify that the proposed model outperforms current state-of-the-art image-to-image translation methods. In contrast with current methods, the proposed approach supports various applications, including style interpolation, content transplantation, and local image translation.

关键词： Training Interpolation computer vision Codes Computational modeling Semantics Self-supervised learning

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Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields

Ref-NeRF: Structured View-Dependent Appearance for Neural Ra...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Verbin, Dor Hedman, Peter Mildenhall, Ben Zickler, Todd Barron, Jonathan T. Srinivasan, Pratul P. Harvard Univ Cambridge MA 02138 USA Google Res Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location. While NeRF-based techniques excel at representing fine geometric structures with smoothly varying view-dependent appearance, they often fail to accurately capture and reproduce the appearance of glossy surfaces. We address this limitation by introducing Ref-NeRF, which replaces NeRF's parameterization of view-dependent outgoing radiance with a representation of reflected radiance and structures this function using a collection of spatially-varying scene properties. We show that together with a regularizer on normal vectors, our model significantly improves the realism and accuracy of specular reflections. Furthermore, we show that our model's internal representation of outgoing radiance is interpretable and useful for scene editing.

关键词： Graphics computer vision Three-dimensional displays Multilayer perceptrons Reflection Sensors pattern recognition

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Knowledge distillation: A good teacher is patient and consistent

Knowledge distillation: A good teacher is patient and consis...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Beyer, Lucas Zhai, Xiaohua Royer, Amelie Markeeva, Larisa Anil, Rohan Kolesnikov, Alexander Google Res Brain Team Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. In this paper we address this issue and significantly bridge the gap between these two types of models. Throughout our empirical investigation we do not aim to necessarily propose a new method, but strive to identify a robust and effective recipe for making state-of-the-art large scale models affordable in practice. We demonstrate that, when performed correctly, knowledge distillation can be a powerful tool for reducing the size of large models without compromising their performance. In particular, we uncover that there are certain implicit design choices, which may drastically affect the effectiveness of distillation. Our key contribution is the explicit identification of these design choices, which were not previously articulated in the literature. We back up our findings by a comprehensive empirical study, demonstrate compelling results on a wide range of vision datasets and, in particular, obtain a state-of-the-art ResNet-50 model for ImageNet, which achieves 82.8% top-1 accuracy.

关键词： Training Manifolds computer vision Schedules Image coding Computational modeling Data models

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Global-Aware Registration of Less-Overlap RGB-D Scans

Global-Aware Registration of Less-Overlap RGB-D Scans

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sun, Che Jia, Yunde Guo, Yi Wu, Yuwei Beijing Inst Technol Sch Comp Sci Beijing Lab Intelligent Informat Technol Beijing 100081 Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a novel method of registering less-overlap RGB-D scans. Our method learns global information of a scene to construct a panorama, and aligns RGB-D scans to the panorama to perform registration. Different from existing methods that use local feature points to register less-overlap RGB-D scans and mismatch too much, we use global information to guide the registration, thereby alleviating the mismatching problem by preserving global consistency of alignments. To this end, we build a scene inference network to construct the panorama representing global information. We introduce a reinforcement learning strategy to iteratively align RGB-D scans with the panorama and refine the panorama representation, which reduces the noise of global information and preserves global consistency of both geometric and photometric alignments. Experimental results on benchmark datasets including SUNCG, Matterport, and ScanNet show the superiority of our method.

关键词： computer vision Three-dimensional displays Reinforcement learning Benchmark testing Sensors Registers pattern recognition

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Comparing Correspondences: Video Prediction with Correspondence-wise Losses

Comparing Correspondences: Video Prediction with Corresponde...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Geng, Daniel Hamilton, Max Owens, Andrew Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image prediction methods often struggle on tasks that require changing the positions of objects, such as video prediction, producing blurry images that average over the many positions that objects might occupy. In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to positional errors: we match the images using optical flow, then measure the visual similarity of corresponding pixels. This change leads to crisper and more perceptually accurate predictions, and does not require modifications to the image prediction network. We apply our method to a variety of video prediction tasks, where it obtains strong performance with simple network architectures, and to the closely related task of video interpolation. Code and results are available at our webpage: https://***/CorrWiseLosses

关键词： Interpolation Visualization Uncertainty Prediction methods computer architecture Position measurement Optical variables measurement

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ConCon-Chi: Concept-Context Chimera Benchmark for Personalized vision-Language Tasks

ConCon-Chi: Concept-Context Chimera Benchmark for Personaliz...

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2024 ieee/CVF conference on computer vision and pattern recognition, cvpr 2024

作者： Rosasco, Andrea Berti, Stefano Pasquale, Giulia Malafronte, Damiano Sato, Shogo Segawa, Hiroyuki Inada, Tetsugo Natale, Lorenzo University of Genoa Italy Istituto Italiano di Tecnologia Italy Sony Interactive Entertainment Inc. Japan

ISBN: (纸本)9798350353006

While recent vision-Language (VL) models excel at open-vocabulary tasks, it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge, where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG, designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large, varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts, we combined different contexts with the same concept, and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties, and we propose principles and a dataset for their performance assessment. Dataset: https://***/10.48557/QJ1166 and code: https://***/hsp-iit/concon-chi-benchmark. © 2024 ieee.

关键词： benchmark chimeric objects compositionality dataset personalization personalized text-to-image generation personalized text-to-image retrieval personalized vision-language models personalized vision-language tasks vision-language models vision-language tasks

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